The rapid advancement of the Internet of Vehicles (IoV) has led to a massive growth in data received from IoV networks. The cloud storage has been a timely service that provides a vast range of data storage for IoV networks. However, existing data storage and access models used to manage and protect data in IoV networks have proven to be insufficient. They are centralized and usually accompanied by a lack of trust, transparency, security, immutability, and provenance. In this paper, we propose VBlock, a blockchain-based system that addresses the issues of illegal modification of outsourced vehicular data for smart city management and improvement. We introduce a novel collusion-resistant model for outsourcing data to cloud storage that ensures the network remains tamper-proof, has good data provenance and auditing, and solves the centralized problems prone to the single point of failure. We introduced a key revocation mechanism to secure the network from malicious nodes. We formally define the system model of VBlock in the setting of a consortium blockchain. Our simulation results and security analysis show that the proposed model provides a strong security guarantee with high efficiency and is practicable in the IoV environment.

The precise detection of liquid flow and viscosity is a crucial challenge in industrial processes and environmental monitoring due to the variety of liquid samples and the complex reflective properties of energetic liquids. Traditional methods often struggle to maintain accuracy under such conditions. This study addresses the complexity arising from sample diversity and the reflective properties of energetic liquids by introducing a novel model based on computer vision and deep learning. We propose the DBN-AGS-FLSS, an integrated deep learning model for high-precision, real-time liquid surface pointer detection. The model combines Deep Belief Networks (DBN), Feedback Least-Squares SVM classifiers (FLSS), and Adaptive Genetic Selectors (AGS). Enhanced by bilateral filtering and adaptive contrast enhancement algorithms, the model significantly improves image clarity and detection accuracy. The use of a feedback mechanism for reverse judgment dynamically optimizes model parameters, enhancing system accuracy and robustness. The model achieved an accuracy, precision, F1 score, and recall of 99.37%, 99.36%, 99.16%, and 99.36%, respectively, with an inference speed of only 1.5 ms/frame. Experimental results demonstrate the model’s superior performance across various complex detection scenarios, validating its practicality and reliability. This study opens new avenues for industrial applications, especially in real-time monitoring and automated systems, and provides valuable reference for future advancements in computer vision-based detection technologies.

Face verification based on ordinary 2D RGB images has been widely used in daily life. However, the quality of ordinary 2D RGB images is limited by illumination, and they lack stereoscopic features, which makes it difficult to apply them in poor lighting conditions and means they are susceptible to interference from head pose and partial occlusions. Considering point clouds are not affected by illumination and can easily represent geometric information, this paper constructs a novel Siamese network for 3D face verification based on Pointnet. In order to reduce the influence of the self-generated point clouds, the chamfer distance is adopted to constrain the original point clouds and explore a new energy function to distinguish features. The experimental results with the Pandora and Curtin Faces datasets show that the accuracy of the proposed method is improved by 0.6% compared with the latest methods; in large pose interference and partial occlusion, the accuracy is improved by 4% and 5%. The results verify that our method outperforms the latest methods and can be applied to a variety of complex scenarios while maintaining real-time performance.

Head pose estimation is an important part of the field of face analysis technology. It can be applied to driver attention monitoring, passenger monitoring, effective information screening, etc. However, illumination changes and partial occlusion interfere with the task, and due to the non-stationary characteristic of the head pose change process, normal regression networks are unable to achieve very accurate results on large-scale synthetic training data. To address the above problems, a Siamese network based on 3D point clouds was proposed, which adopts a share weight network with similar pose samples to constrain the regression process of the pose’s angles; meanwhile, a local feature descriptor was introduced to describe the local geometric features of the objects. In order to verify the performance of our method, we conducted experiments on two public datasets: the Biwi Kinect Head Pose dataset and Pandora. The results show that compared with the latest methods, our standard deviation was reduced by 0.4, and the mean error was reduced by 0.1; meanwhile, our network also maintained a good real-time performance.

In order to realize a general-purpose automatic formal verification platform based on WebAssembly technology as a web service (FVPS), which aims to provide an automated report of vulnerability detections, this work builds a Hyperledger Fabric blockchain runtime model. It proposes an optimized methodology of the functional equivalent translation from source program languages to formal languages. This methodology utilizes an external application programming interface (API) table to replace the source codes in compilation, thereby pruning the part of housekeeping codes to ease code inflation. Code inflation is a significant metric in formal language translation. Namely, minor code inflation enhances verification scale and performance efficiency. It determines the efficiency of formal verification, involving launching, running, and memory usage. For instance, path explosion increases exponentially, resulting in out-of-memory. The experimental results conclude that program languages like golang severely impact code inflation. FVPS reduces the wasm code size by over 90%, achieving two orders of optimization magnitude, from 2000 kilobyte (KB) to 90 KB. That means we can cope with golang applications up to 20 times larger than the original in scale. This work eliminates the gap between Hyperledger Fabric smart contracts and WebAssembly. Our approach is pragmatic, adaptable, extendable, and flexible. Nowadays, FVPS is successfully applied in a Railway-Port-Aviation blockchain transportation system.